This package is meant to be used together with Chloe (Boussard and Baudry 2017). Chloe is used to computed your landscape metrics.
The csv must be formatted like:
## ID X Y
## 1 PF_B03 357690.3335 6829670.889
## 2 PF_B10 364265.2261 6831715.825
## 3 PF_B09 363789.7071 6832120.265
## 4 PF_B01 357800.5726 6825248.985
## 5 PF_B02 359015.1017 6827044.28
## 6 PF_B05 359886.9637 6837457.346
ID is the name of your sampling points.
After that you can start running the analysis on Chloe following 10 step.
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Step 8
Step 9
To test the correlations across metrics two solutions are offered:
If your sampling units are distributed randomly across all cover types you want to computed the metrics across all the landscape.
If your sampling units are distributed uniformly in only one type of cover (e.g. the sampling was made only in wood), then you want to generate a random number of points in this cover to computed metrics.
You probably choosed a lot of metrics and scales to test. The analysis will become a lot more heaver for Chloe to handle. So now we will start to use Chloe in R to computed metrics.
You will also need the package “Multi”.
To install the package “Multi” follow this procedure below:
install.packages("devtools",repos = "http://cran.us.r-project.org")
library(devtools)
install_github("Pintademijote/multipack")
install.packages("FactoMineR",repos = "http://cran.us.r-project.org")
library(Multi)
To call Chloe on R, first use the code above below (in the case that you installed Chloe in the default folder):
source("C:/Users/Public/Inra/Chloe/R/chloe.R")
cl <- Chloe()
You also want to load the set of metrics and scales you choosed for the Analysis:
scales=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3.properties","Distance")
dist=as.numeric(scales)
metrics=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3.properties","Metrics")
scales=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3.properties","Distance")
dist=as.numeric(scales)
metrics=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3.properties","Metrics")
This section will explain the step on R to do the multiscales analysis of your Chloe output results.
library(Multi)
metrics=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3_grain.properties","Metrics")
scales=read.Chloe.properties("C:/Users/pglem/Documents/Master/Stage M2/Données/batch_test3_grain.properties","Distance")
chloe=read.csv("C:/Users/pglem/Documents/Master/Stage M2/Données/Results3/cover_2016_cr.csv", h=T, sep=";",check.names = F)
Boussard, Hugues, and Jacques Baudry. 2017. “Chloe4.0: A Software for Landscape Pattern Analysis.”